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Found 35 papers, 8 papers with code
Comparison of gait phase detection using traditional machine learning and deep learning techniques
The results show up to 75% average accuracy for traditional ML models and 79% for Deep Learning (DL) model.
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments
In this paper, we introduce Optimal Transport Reward (OTR) labelling, an innovative algorithm designed to assign rewards to offline trajectories, using a small number of high-quality expert demonstrations.
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
Overall, the goal of the paper is to provide a comprehensive guide to the growing field of IL in robotics and AI.
Uncertainty Aware Neural Network from Similarity and Sensitivity
In the proposed NN training method for UQ, first, we train a shallow NN for the point prediction.
A Review of Deep Learning for Video Captioning
Video captioning (VC) is a fast-moving, cross-disciplinary area of research that bridges work in the fields of computer vision, natural language processing (NLP), linguistics, and human-computer interaction.
Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing
In this paper, we present a systematic review of the prediction with the reject option in the context of various neural networks.
SFE: A Simple, Fast and Efficient Feature Selection Algorithm for High-Dimensional Data
The results obtained indicate that the two proposed algorithms significantly outperform the other algorithms, and can be used as efficient and effective algorithms in selecting features from high-dimensional datasets.
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined.
Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities.
Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression
So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians.
Controlled Dropout for Uncertainty Estimation
In this study, we present a new version of the traditional dropout layer where we are able to fix the number of dropout configurations.
DoubleU-Net++: Architecture with Exploit Multiscale Features for Vertebrae Segmentation
Also, for xVertSeg dataset, we achieved precision, recall, and F1-score of above 97% for sagittal view, above 93% for coronal view , and above 96% for axial view.
A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification
We also apply their usual fully-connected layer and the Spinal fully-connected layer to investigate the effectiveness of SpinalNet.
Ranked #1 on
Fine-Grained Image Classification
on Bird-225
(using extra training data)
Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression
The current survey proposes a triangular type-2 fuzzy regression (TT2FR) model to ameliorate the efficiency of the model by handling the uncertainty in the data.
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies
The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands.
MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification
It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUamodelhas achieved a high accuracy of 98. 11% on a breast cancer histology image dataset.
Breast Cancer Histology Image Classification
Classification
+2
Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning
Countless research works of deep neural networks (DNNs) in the task of credit card fraud detection have focused on improving the accuracy of point predictions and mitigating unwanted biases by building different network architectures or learning models.
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products
Secondly, to achieve a reliable classification and to measure epistemic uncertainty, we employ an uncertainty quantification (UQ) technique (ensemble of MLP models) using features extracted from four pre-trained CNNs.
Confidence Aware Neural Networks for Skin Cancer Detection
Deep learning (DL) models have received particular attention in medical imaging due to their promising pattern recognition capabilities.
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction.
UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection
Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images.
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods
This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series.
Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients
To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.
Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images
In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images.
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data
Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data.
Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection
Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates.
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes.
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)
We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science.
An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosis
The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak.
SpinalNet: Deep Neural Network with Gradual Input
Traditional learning with ImageNet pre-trained initial weights and SpinalNet classification layers provided the SOTA performance on STL-10, Fruits 360, Bird225, and Caltech-101 datasets.
Ranked #1 on
Satellite Image Classification
on STL-10, 40 Labels
Fine-Grained Image Classification
Satellite Image Classification
+1
Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review
Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging.
Epileptic Seizures Detection Using Deep Learning Techniques: A Review
The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed.
Material Recognition for Automated Progress Monitoring using Deep Learning Methods
Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation.
Optimal Uncertainty-guided Neural Network Training
Most of the existing cost functions of uncertainty guided NN training are not customizable and the convergence of training is uncertain.
Wind ramp event prediction with parallelized Gradient Boosted Regression Trees
Accurate prediction of wind ramp events is critical for ensuring the reliability and stability of the power systems with high penetration of wind energy.
